Abstract
Object detection and tracking is an exciting and interesting research area in the field of computer vision, and its technologies have been widely used in various applications such as surveillance, military and augmented reality. This paper suggests and implements a robust object detection and tracking scheme to localize and to track multiple objects from input images, which estimates target state using the likelihoods obtained from convolutional neural networks. As the experimental results, the proposed system is effective to handle multiple target appearances and other exceptions, and it is able to detect the interesting object accurately in various environments, compared to the traditional method.
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This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2018R1A2B6008255).
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Lee, YH., Lee, WB. (2020). Object Detection and Tracking Based on Deep Learning. In: Barolli, L., Xhafa, F., Hussain, O. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing . IMIS 2019. Advances in Intelligent Systems and Computing, vol 994. Springer, Cham. https://doi.org/10.1007/978-3-030-22263-5_59
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DOI: https://doi.org/10.1007/978-3-030-22263-5_59
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